Seeing Beyond Vision

From pixels to latent imagination — robots that see beyond the image.

Challenge uncertainty, Change perception through latent dynamics, and drive Impact toward uncertainty-aware embodied intelligence.

Deep Stochastic State-Space Models (Deep SSSM)

Deep SSSM pipeline: Observation → Latent State Inference → Stochastic Prediction → Control & Planning → Feedback

Perception → Inference → Prediction → Planning → Action

Robots learn to act beyond direct observation through stochastic world modeling.

Deep SSSMs are probabilistic world models that fuse stochastic latent dynamics with control-theoretic planning. They enable robots to reason about hidden or occluded states, predict environment evolution, and plan actions under uncertainty.

What Is Deep SSSM?

A Deep Stochastic State-Space Model learns to represent complex, partially observable systems via latent states that evolve probabilistically:

s_t+1 ∼ p(s_t+1|s_t, a_t), o_t ∼ p(o_t|s_t)

Instead of relying only on visible cues, Deep SSSM constructs an internal probabilistic world — a compact, uncertainty-aware abstraction of how the system truly evolves beneath noisy or occluded observations.

⚙️ Why It Matters

Robotic systems often face occlusion, sensor noise, and partial visibility. Deterministic models assume full observability and collapse uncertainty into one estimate, yielding overconfident and brittle predictions. The key challenge is to represent uncertainty explicitly while remaining computationally efficient for real-time control.

🌌 How Deep SSSM Addresses This

Stochastic latent transition: captures multi-modal futures and propagates uncertainty forward.
Probabilistic encoder: filters occluded observations into calibrated latent beliefs.
Variational inference: jointly optimizes prediction and uncertainty calibration.

Rather than predicting a single trajectory, Deep SSSM models distributions over future states, enabling uncertainty-aware planning and control that stay stable under occlusion, drift, or unseen disturbances.

🤖 Core Insight

Deep SSSM turns perception + control into a single probabilistic reasoning process — where uncertainty is not a nuisance to remove, but a signal to learn, model, and exploit.

Impact and Applications

Deep SSSM establishes a foundation for occlusion-robust visual control and uncertainty-aware decision-making in robotics. Its implications span:

Vision-based manipulation under partial observability
Human–robot collaboration and shared autonomy
Predictive control under dynamic, uncertain environments

Publication

Deep Stochastic State-Space Modeling for Occlusion-Robust Robotic Manipulation.